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As Indian companies across industries increasingly embrace data-driven decision-making, artificial intelligence (AI), and automation, the demand for skilled data scientists continues to surge. Data Visualization: Ability to create intuitive visualizations using Matplotlib, Seaborn, Tableau, or PowerBI to convey insights clearly.
Summary: The article explores the differences between data driven and AI driven practices. Data-driven and AI-driven approaches have become key in how businesses address challenges, seize opportunities, and shape their strategic directions.
Summary: Business Analytics focuses on interpreting historical data for strategic decisions, while Data Science emphasizes predictive modeling and AI. Dashboards, such as those built using Tableau or PowerBI , provide real-time visualizations that help track key performance indicators (KPIs).
These frameworks facilitate the efficient processing of Big Data, enabling organisations to derive insights quickly.Some popular frameworks include: ApacheHadoop: An open-source framework that allows for distributed processing of large datasets across clusters of computers. It is known for its high fault tolerance and scalability.
These frameworks facilitate the efficient processing of Big Data, enabling organisations to derive insights quickly.Some popular frameworks include: ApacheHadoop: An open-source framework that allows for distributed processing of large datasets across clusters of computers. It is known for its high fault tolerance and scalability.
ETL Tools: Apache NiFi, Talend, etc. Big Data Processing: ApacheHadoop, Apache Spark, etc. Excel, Tableau, PowerBI, SQL Server, MySQL, Google Analytics, etc. The post The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering appeared first on Pickl AI.
In der Parallelwelt der ITler wurde das Tool und Ökosystem ApacheHadoop quasi mit Big Data beinahe synonym gesetzt. Artificial Intelligence (AI) ersetzt. AI wiederum scheint spätestens mit ChatGPT 2022/2023 eine neue Euphorie-Phase erreicht zu haben, mit noch ungewissem Ausgang. Industrie 4.0). Process Mining).
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